'''simple docstring'''




import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__UpperCAmelCase					=getLogger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__ = 8							,    UpperCamelCase__ = 10_24							,    UpperCamelCase__="val"							,    UpperCamelCase__=None							,    UpperCamelCase__=False							,    UpperCamelCase__="summarization"							,    UpperCamelCase__=None							,    UpperCamelCase__=1							,    UpperCamelCase__ = None							,    UpperCamelCase__=""							,    **UpperCamelCase__							,    )      ->       Dict:
    __lowerCamelCase            =    str(UpperCamelCase__							)
    assert local_rank is not None
    torch.distributed.init_process_group(backend='''nccl'''							,    rank=UpperCamelCase__							)

    __lowerCamelCase            =    Path(UpperCamelCase__							)
    __lowerCamelCase            =    save_dir.joinpath(f"""rank_{local_rank}_output.json"""							)
    torch.cuda.set_device(UpperCamelCase__							)
    __lowerCamelCase            =    AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__							).cuda()
    if fpaa:
        __lowerCamelCase            =    model.half()
    # determine if we need to increase num_beams
    use_task_specific_params(UpperCamelCase__							,    UpperCamelCase__							)  # update config with task specific params
    __lowerCamelCase            =    generate_kwargs.pop('''num_beams'''							,    model.config.num_beams							)  # AttributeError risk?
    if num_return_sequences > num_beams:
        __lowerCamelCase            =    num_return_sequences

    __lowerCamelCase            =    AutoTokenizer.from_pretrained(UpperCamelCase__							)
    logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""							)  # if this is wrong, check config.model_type.

    if max_source_length is None:
        __lowerCamelCase            =    tokenizer.model_max_length
    if prefix is None:
        __lowerCamelCase            =    prefix or getattr(model.config							,    '''prefix'''							,    ''''''							) or ''''''
    __lowerCamelCase            =    SeqaSeqDataset(
        UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    max_target_length=10_24							,    type_path=UpperCamelCase__							,    n_obs=UpperCamelCase__							,    prefix=UpperCamelCase__							,    **UpperCamelCase__							,    )
    # I set shuffle=True for a more accurate progress bar.
    # If all the longest samples are first, the prog bar estimate is too high at the beginning.
    __lowerCamelCase            =    ds.make_sortish_sampler(UpperCamelCase__							,    distributed=UpperCamelCase__							,    add_extra_examples=UpperCamelCase__							,    shuffle=UpperCamelCase__							)
    __lowerCamelCase            =    DataLoader(UpperCamelCase__							,    sampler=UpperCamelCase__							,    batch_size=UpperCamelCase__							,    collate_fn=ds.collate_fn							)
    __lowerCamelCase            =    []
    for batch in tqdm(UpperCamelCase__							):
        __lowerCamelCase            =    model.generate(
            input_ids=batch['''input_ids'''].to(model.device							)							,    attention_mask=batch['''attention_mask'''].to(model.device							)							,    num_return_sequences=UpperCamelCase__							,    num_beams=UpperCamelCase__							,    **UpperCamelCase__							,    )
        __lowerCamelCase            =    tokenizer.batch_decode(UpperCamelCase__							,    skip_special_tokens=UpperCamelCase__							,    clean_up_tokenization_spaces=UpperCamelCase__							)
        __lowerCamelCase            =    batch['''ids''']
        if num_return_sequences > 1:
            __lowerCamelCase            =    chunks(UpperCamelCase__							,    UpperCamelCase__							)  # batch size chunks, each of size num_return_seq
        for i, pred in enumerate(UpperCamelCase__							):
            results.append({'''pred''': pred, '''id''': ids[i].item()}							)
    save_json(UpperCamelCase__							,    UpperCamelCase__							)
    return results, sampler.num_replicas






def __lowerCAmelCase					(				)      ->       List[str]:
    __lowerCamelCase            =    argparse.ArgumentParser(
        epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''							)
    parser.add_argument('''--data_dir'''							,    type=UpperCamelCase__							,    help='''like cnn_dm/test.source'''							)
    parser.add_argument(
        '''--model_name'''							,    type=UpperCamelCase__							,    help='''like facebook/bart-large-cnn,t5-base, etc.'''							,    default='''sshleifer/distilbart-xsum-12-3'''							,    )
    parser.add_argument('''--save_dir'''							,    type=UpperCamelCase__							,    help='''where to save'''							,    default='''tmp_gen'''							)
    parser.add_argument('''--max_source_length'''							,    type=UpperCamelCase__							,    default=UpperCamelCase__							)
    parser.add_argument(
        '''--type_path'''							,    type=UpperCamelCase__							,    default='''test'''							,    help='''which subset to evaluate typically train/val/test'''							)
    parser.add_argument('''--task'''							,    type=UpperCamelCase__							,    default='''summarization'''							,    help='''used for task_specific_params + metrics'''							)
    parser.add_argument('''--bs'''							,    type=UpperCamelCase__							,    default=8							,    required=UpperCamelCase__							,    help='''batch size'''							)
    parser.add_argument(
        '''--local_rank'''							,    type=UpperCamelCase__							,    default=-1							,    required=UpperCamelCase__							,    help='''should be passed by distributed.launch'''							)

    parser.add_argument(
        '''--n_obs'''							,    type=UpperCamelCase__							,    default=UpperCamelCase__							,    required=UpperCamelCase__							,    help='''How many observations. Defaults to all.'''							)
    parser.add_argument(
        '''--num_return_sequences'''							,    type=UpperCamelCase__							,    default=1							,    required=UpperCamelCase__							,    help='''How many sequences to return'''							)
    parser.add_argument(
        '''--sync_timeout'''							,    type=UpperCamelCase__							,    default=6_00							,    required=UpperCamelCase__							,    help='''How long should master process wait for other processes to finish.'''							,    )
    parser.add_argument('''--src_lang'''							,    type=UpperCamelCase__							,    default=UpperCamelCase__							,    required=UpperCamelCase__							)
    parser.add_argument('''--tgt_lang'''							,    type=UpperCamelCase__							,    default=UpperCamelCase__							,    required=UpperCamelCase__							)
    parser.add_argument(
        '''--prefix'''							,    type=UpperCamelCase__							,    required=UpperCamelCase__							,    default=UpperCamelCase__							,    help='''will be added to the begininng of src examples'''							)
    parser.add_argument('''--fp16'''							,    action='''store_true'''							)
    parser.add_argument('''--debug'''							,    action='''store_true'''							)
    __lowerCamelCase            =    time.time()
    __lowerCamelCase      ,    __lowerCamelCase            =    parser.parse_known_args()
    __lowerCamelCase            =    parse_numeric_n_bool_cl_kwargs(UpperCamelCase__							)
    if generate_kwargs and args.local_rank <= 0:
        print(f"""parsed the following generate kwargs: {generate_kwargs}"""							)
    __lowerCamelCase            =    Path(args.save_dir + '''_tmp'''							)
    Path(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)  # this handles locking.
    __lowerCamelCase            =    list(json_save_dir.glob('''rank_*.json'''							)							)
    if intermediate_files:
        raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""							)
        # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
    __lowerCamelCase            =    {}
    if args.src_lang is not None:
        __lowerCamelCase            =    args.src_lang
    if args.tgt_lang is not None:
        __lowerCamelCase            =    args.tgt_lang

    Path(args.save_dir							).mkdir(exist_ok=UpperCamelCase__							)
    __lowerCamelCase      ,    __lowerCamelCase            =    eval_data_dir(
        args.data_dir							,    UpperCamelCase__							,    args.model_name							,    type_path=args.type_path							,    bs=args.bs							,    fpaa=args.fpaa							,    task=args.task							,    local_rank=args.local_rank							,    n_obs=args.n_obs							,    max_source_length=args.max_source_length							,    num_return_sequences=args.num_return_sequences							,    prefix=args.prefix							,    dataset_kwargs=UpperCamelCase__							,    **UpperCamelCase__							,    )

    if args.local_rank <= 0:
        __lowerCamelCase            =    Path(args.save_dir							)
        save_dir.mkdir(exist_ok=UpperCamelCase__							)
        __lowerCamelCase            =    gather_results_from_each_node(UpperCamelCase__							,    UpperCamelCase__							,    args.sync_timeout							)
        __lowerCamelCase            =    combine_partial_results(UpperCamelCase__							)
        if args.num_return_sequences > 1:
            __lowerCamelCase            =    save_dir.joinpath('''pseudolabel_results.json'''							)
            print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""							)
            save_json(UpperCamelCase__							,    UpperCamelCase__							)
            return
        __lowerCamelCase            =    Path(args.data_dir							).joinpath(args.type_path + '''.target'''							)
        with open(UpperCamelCase__							) as f:
            __lowerCamelCase            =    [x.rstrip() for x in f.readlines()][: len(UpperCamelCase__							)]

        # Calculate metrics, save metrics,  and save _generations.txt
        __lowerCamelCase            =    '''translation''' in args.task
        __lowerCamelCase            =    calculate_bleu if calc_bleu else calculate_rouge
        __lowerCamelCase            =    '''bleu''' if calc_bleu else '''rouge'''
        __lowerCamelCase            =    score_fn(UpperCamelCase__							,    UpperCamelCase__							)
        __lowerCamelCase            =    len(UpperCamelCase__							)
        __lowerCamelCase            =    time.time() - start_time
        __lowerCamelCase            =    round(runtime / metrics['''n_obs''']							,    4							)
        __lowerCamelCase            =    num_replicas
        # TODO(@stas00): add whatever metadata to metrics
        __lowerCamelCase            =    save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""							)
        save_json(UpperCamelCase__							,    UpperCamelCase__							,    indent=UpperCamelCase__							)
        print(UpperCamelCase__							)
        write_txt_file(UpperCamelCase__							,    save_dir.joinpath(f"""{args.type_path}_generations.txt"""							)							)
        if args.debug:
            write_txt_file(UpperCamelCase__							,    save_dir.joinpath(f"""{args.type_path}.target"""							)							)
        else:
            shutil.rmtree(UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List:
    __lowerCamelCase            =    []
    for partial_result in partial_results:
        records.extend(UpperCamelCase__							)
    __lowerCamelCase            =    sorted(UpperCamelCase__							,    key=lambda UpperCamelCase__							: x["id"]							)
    __lowerCamelCase            =    [x['''pred'''] for x in records]
    return preds






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Dict[str, List]]:
    # WAIT FOR lots of .json files
    __lowerCamelCase            =    time.time()
    logger.info('''waiting for all nodes to finish'''							)
    __lowerCamelCase            =    None
    while (time.time() - start_wait) < timeout:
        __lowerCamelCase            =    list(save_dir.glob('''rank_*.json'''							)							)
        if len(UpperCamelCase__							) < num_replicas:
            continue
        try:
            # make sure all json files are fully saved
            __lowerCamelCase            =    lmap(UpperCamelCase__							,    UpperCamelCase__							)
            return json_data
        except JSONDecodeError:
            continue
    else:
        raise TimeoutError('''Rank 0 gave up on waiting for other processes'''							)
    # Unreachable


if __name__ == "__main__":
  # Usage for MT:
  run_generate()

'''simple docstring'''




from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__UpperCAmelCase					=logging.get_logger(__name__)




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Union[str, Any]				=["audio_values", "audio_mask"]






     def __init__(					self						:						List[str]    ,							a						:						List[Any]=20_48    ,							a						:						Optional[int]=1    ,							a						:						List[str]=[16, 16]    ,							a						:						int=1_28    ,							a						:						Dict=4_41_00    ,							a						:						str=86    ,							a						:						int=20_48    ,							a						:						int=0.0    ,							**a						:						Dict    ,							):





         """simple docstring"""



         super().__init__(
             feature_size=a    ,							sampling_rate=a    ,							padding_value=a    ,							**a    ,							)

         __lowerCamelCase            =    spectrogram_length
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    patch_size
         __lowerCamelCase            =    feature_size // self.patch_size[1]
         __lowerCamelCase            =    n_fft
         __lowerCamelCase            =    sampling_rate // hop_length_to_sampling_rate
         __lowerCamelCase            =    sampling_rate
         __lowerCamelCase            =    padding_value
         __lowerCamelCase            =    mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2    ,							num_mel_filters=a    ,							min_frequency=0.0    ,							max_frequency=2_20_50.0    ,							sampling_rate=a    ,							norm='''slaney'''    ,							mel_scale='''slaney'''    ,							).T






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple    ,							a						:						np.array       ):





         """simple docstring"""



         __lowerCamelCase            =    spectrogram(
             a    ,							window_function(self.n_fft    ,							'''hann'''       )    ,							frame_length=self.n_fft    ,							hop_length=self.hop_length    ,							power=2.0    ,							mel_filters=self.mel_filters.T    ,							log_mel='''dB'''    ,							db_range=80.0    ,							)
         __lowerCamelCase            =    log_spec[:, :-1]
         __lowerCamelCase            =    log_spec - 20.0
         __lowerCamelCase            =    np.clip(log_spec / 40.0    ,							-2.0    ,							0.0       ) + 1.0
         return log_spec






     def __call__(					self						:						Union[str, Any]    ,							a						:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,							a						:						Optional[Union[str, TensorType]] = None    ,							a						:						Optional[bool] = True    ,							a						:						Optional[int] = None    ,							a						:						bool = False    ,							a						:						bool = False    ,							**a						:						int    ,							):





         """simple docstring"""



         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     '''This feature extractor is set to support sampling rate'''
                     f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
                     f""" with {self.sampling_rate} and not {sampling_rate}."""       )
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''       )

         __lowerCamelCase            =    isinstance(a    ,							np.ndarray       ) and len(raw_speech.shape       ) > 1
         if is_batched_numpy and len(raw_speech.shape       ) > 2:
             raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""       )
         __lowerCamelCase            =    is_batched_numpy or (
             isinstance(a    ,							(list, tuple)       ) and (isinstance(raw_speech[0]    ,							(np.ndarray, tuple, list)       ))
         )
         if is_batched:
             __lowerCamelCase            =    [np.asarray([speech]    ,							dtype=np.floataa       ).T for speech in raw_speech]
         elif not is_batched and not isinstance(a    ,							np.ndarray       ):
             __lowerCamelCase            =    np.asarray(a    ,							dtype=np.floataa       )
         elif isinstance(a    ,							np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
             __lowerCamelCase            =    raw_speech.astype(np.floataa       )
         # always return batch
         if not is_batched:
             __lowerCamelCase            =    [np.asarray([raw_speech]       ).T]

         # Convert audio signals to log mel spectrograms, truncate by time axis
         __lowerCamelCase            =    [
             self._np_extract_fbank_features(waveform.squeeze()       ).T[: self.spectrogram_length] for waveform in raw_speech
         ]
         if isinstance(audio_features[0]    ,							a       ):
             __lowerCamelCase            =    [np.asarray(a    ,							dtype=np.floataa       ) for feature in audio_features]

         # Create audio attention mask
         __lowerCamelCase            =    max(
             [ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len for feature in audio_features]       )  # The maximum number of audio patches in a batch
         if return_attention_mask:
             __lowerCamelCase            =    [
                 (ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             __lowerCamelCase            =    np.array(a       ).astype(np.floataa       )

         # convert into correct format for padding
         __lowerCamelCase            =    max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
         __lowerCamelCase            =    np.ones([len(a       ), 1, max_time_len, self.feature_size]       ).astype(np.floataa       )
         __lowerCamelCase            =    padded_audio_features * self.padding_value
         for i in range(len(a       )       ):
             __lowerCamelCase            =    audio_features[i]
             __lowerCamelCase            =    feature

         # return as BatchFeature
         if return_attention_mask:
             __lowerCamelCase            =    {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
         else:
             __lowerCamelCase            =    {'''audio_values''': padded_audio_features}

         __lowerCamelCase            =    BatchFeature(data=a    ,							tensor_type=a       )
         return encoded_inputs
